doc: added Dataset card

README.md

@@ -190,4 +190,83 @@ configs:
     path: technology/upper_direct-*
   - split: lower_direct
     path: technology/lower_direct-*
+license: cc-by-sa-4.0
+task_categories:
+- question-answering
+- multiple-choice
+language:
+- en
+tags:
+- knowledge-probing
+- llm-evaluation
+- entity-resolution
+- machine-unlearning
+size_categories:
+- 10K<n<100K
 ---
+
+# ShadowBench: A Hardened Benchmark for Latent Entity Association
+
+**ShadowBench** is a diagnostic framework designed to evaluate the "Shadow Knowledge" of Large Language Models (LLMs). While traditional benchmarks measure factual recall using explicit entity names (e.g., "Elon Musk"), ShadowBench evaluates whether a model can navigate its internal knowledge graph when these **lexical anchors** are removed.
+
+## Dataset Summary
+The core task in ShadowBench is **Dual-Trait Association (DTA)**. A model is presented with an anonymized shadow description (Trait A) and must associate it with a second, independent fact (Trait B) among three "Hard Negative" distractors. 
+
+Success requires the model to utilize the hidden entity as a semantic bridge:
+
+`Trait A (Shadow)` &rarr; `[Latent Entity]` &rarr; `Trait B (Target Choice)`
+
+### Key Features
+*   **Adversarially Hardened:** Unlike standard MCQs, ShadowBench (v3) is filtered to prevent "shortcut learning" via gendered pronouns, chronological era-matching, or category-leaks.
+*   **Scale Robust:** Evaluated on models ranging from 8B parameters (Llama-3, Qwen3) to frontier scales (GPT-5.4).
+*   **Multi-Domain:** Covers Technology, Sports (Tennis), and Entertainment (Actors).
+*   **Stratified:** Includes "Upper Tier" (Head) and "Lower Tier" (Tail) entities based on Wikipedia popularity metrics to evaluate "Popularity Bias."
+
+## Dataset Structure
+
+### Subsets
+The dataset is divided into three primary domains:
+*   `technology`: Corporate, product, and leadership-based associations.
+*   `sports`: Numerical achievements and career milestones in professional tennis.
+*   `entertainment`: Narrative roles and filmographic associations.
+
+### Splits
+Each subset contains the following splits:
+*   `upper_shadow` / `lower_shadow`: The primary anonymized DTA task.
+*   `upper_direct` / `lower_direct`: A control split where explicit names are restored to establish a factual "ceiling" (Direct QA).
+*   `upper_controlled` / `lower_controlled`: A 1:1 entity-matched subset used for sensitivity analysis.
+
+### Data Schema
+Each sample contains:
+- `entity`: The hidden entity name.
+- `question`: The shadow description (Trait A).
+- `choices`: A dictionary (A, B, C, D) containing Trait B and three hard distractors.
+- `answer`: The correct option key.
+- `metadata`: A mapping dictionary where each key (A, B, C, D) corresponds to the actual entity represented by that answer choice.
+
+## Construction & Hardening (v1 to v3)
+ShadowBench was developed through an iterative process to ensure success is strictly contingent on latent semantic reasoning:
+1.  **v1:** Lexical Anonymization (Names removed).
+2.  **v2:** Chronological & Syntactic Hardening (Pronouns neutralized + Generational Proximity Filter added).
+3.  **v3:** Demographic Homogeneity (Gender-matched distractors added to prevent elimination via lexical cues like "WTA" or "Best Actress").
+
+## Usage
+You can load this dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the Technology Shadow split
+dataset = load_dataset("shadow-bench/ShadowBench", "technology", split="upper_shadow")
+
+# Inspect a sample
+print(dataset[0])
+```
+
+## Licensing
+
+This dataset is derived from Wikipedia and is licensed under CC BY-SA 4.0.
+
+## Citation
+
+If you use this dataset in your research, please cite our paper: [TBD]